

"""simple docstring"""




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_lowercase   :	int												=						logging.getLogger(__name__)
class      _UpperCAmelCase   :



	def __init__(  self     :       Tuple						):
		__UpperCAmelCase      		= False



	def        a  (  self     :       int			,    _lowercase     :       Tuple			,    _lowercase     :       Any			,    _lowercase     :       List[Any]			,    _lowercase     :       Tuple						):
		if not self.initialized:
			__UpperCAmelCase      		= RagRetriever(
			    _lowercase			,    question_encoder_tokenizer=_lowercase			,    generator_tokenizer=_lowercase			,    index=_lowercase			,    init_retrieval=_lowercase			,    )
			__UpperCAmelCase      		= True



	def        a  (  self     :       Optional[int]						):
		self.retriever.index.init_index()



	def        a  (  self     :       Any			,    _lowercase     :       Any			,    _lowercase     :       Optional[Any]						):
		__UpperCAmelCase		,							__UpperCAmelCase      		= self.retriever._main_retrieve(_lowercase			,    _lowercase						)
		return doc_ids, retrieved_doc_embeds






class      _UpperCAmelCase   (							_lowerCAmelCase       ):



	def __init__(  self     :       List[Any]			,    _lowercase     :       Dict			,    _lowercase     :       List[str]			,    _lowercase     :       List[Any]			,    _lowercase     :       int			,    _lowercase     :       Union[str, Any]=None						):
		if index is not None and index.is_initialized() and len(_lowercase						) > 0:
			raise ValueError(
			    '''When using Ray for distributed fine-tuning, '''
			    '''you\'ll need to provide the paths instead, '''
			    '''as the dataset and the index are loaded '''
			    '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''						)
		super().__init__(
		    _lowercase			,    question_encoder_tokenizer=_lowercase			,    generator_tokenizer=_lowercase			,    index=_lowercase			,    init_retrieval=_lowercase			,    )
		__UpperCAmelCase      		= retrieval_workers
		if len(self.retrieval_workers						) > 0:
			ray.get(
			    [
			        worker.create_rag_retriever.remote(_lowercase			,    _lowercase			,    _lowercase			,    _lowercase						)
			        for worker in self.retrieval_workers
			    ]						)



	def        a  (  self     :       List[str]						):
		logger.info('''initializing retrieval'''						)

		if len(self.retrieval_workers						) > 0:
			ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]						)
		else:
			# Non-distributed training. Load index into this same process.
			self.index.init_index()



	def        a  (  self     :       int			,    _lowercase     :       str			,    _lowercase     :       Tuple						):
		if len(self.retrieval_workers						) > 0:
			# Select a random retrieval actor.
			__UpperCAmelCase      		= self.retrieval_workers[random.randint(0			,    len(self.retrieval_workers						) - 1						)]
			__UpperCAmelCase		,							__UpperCAmelCase      		= ray.get(random_worker.retrieve.remote(_lowercase			,    _lowercase						)						)
		else:
			__UpperCAmelCase		,							__UpperCAmelCase      		= self._main_retrieve(_lowercase			,    _lowercase						)
		return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_lowercase						)



	@classmethod
	def        a  (  cls     :       Union[str, Any]			,    _lowercase     :       int			,    _lowercase     :       List[Any]=None			,    **_lowercase     :       str						):
		return super(_lowercase			,    cls						).get_tokenizers(_lowercase			,    _lowercase			,    **_lowercase						)



	@classmethod
	def        a  (  cls     :       Optional[Any]			,    _lowercase     :       Union[str, Any]			,    _lowercase     :       List[Any]			,    _lowercase     :       Any=None			,    **_lowercase     :       Union[str, Any]						):
		__UpperCAmelCase      		= kwargs.pop('''config'''			,    _lowercase						) or RagConfig.from_pretrained(_lowercase			,    **_lowercase						)
		__UpperCAmelCase      		= RagTokenizer.from_pretrained(_lowercase			,    config=_lowercase						)
		__UpperCAmelCase      		= rag_tokenizer.question_encoder
		__UpperCAmelCase      		= rag_tokenizer.generator
		if indexed_dataset is not None:
			__UpperCAmelCase      		= '''custom'''
			__UpperCAmelCase      		= CustomHFIndex(config.retrieval_vector_size			,    _lowercase						)
		else:
			__UpperCAmelCase      		= cls._build_index(_lowercase						)
		return cls(
		    _lowercase			,    question_encoder_tokenizer=_lowercase			,    generator_tokenizer=_lowercase			,    retrieval_workers=_lowercase			,    index=_lowercase			,    )







"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'fnet'





      def __init__(			self ,		lowercase=32_000 ,		lowercase=768 ,		lowercase=12 ,		lowercase=3_072 ,		lowercase="gelu_new" ,		lowercase=0.1 ,		lowercase=512 ,		lowercase=4 ,		lowercase=0.02 ,		lowercase=1e-12 ,		lowercase=False ,		lowercase=512 ,		lowercase=3 ,		lowercase=1 ,		lowercase=2 ,		**lowercase ,		)						->	int:
            super().__init__(pad_token_id=lowercase ,		bos_token_id=lowercase ,		eos_token_id=lowercase ,		**lowercase					)

            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			max_position_embeddings
            lowerCAmelCase    		=			hidden_size
            lowerCAmelCase    		=			num_hidden_layers
            lowerCAmelCase    		=			intermediate_size
            lowerCAmelCase    		=			hidden_act
            lowerCAmelCase    		=			hidden_dropout_prob
            lowerCAmelCase    		=			initializer_range
            lowerCAmelCase    		=			type_vocab_size
            lowerCAmelCase    		=			layer_norm_eps
            lowerCAmelCase    		=			use_tpu_fourier_optimizations
            lowerCAmelCase    		=			tpu_short_seq_length






